{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "cf9a11e5",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3410558f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "最近 2 年上涨月数/总月数：13/25 | 概率：52.00%\n",
      "最近 3 年上涨月数/总月数：19/37 | 概率：51.35%\n",
      "最近 5 年上涨月数/总月数：31/61 | 概率：50.82%\n",
      "最近 8 年上涨月数/总月数：53/97 | 概率：54.64%\n",
      "最近 10 年上涨月数/总月数：63/121 | 概率：52.07%\n",
      "最近 13 年上涨月数/总月数：68/126 | 概率：53.97%\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import akshare as ak\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import datetime\n",
    "import numpy as np\n",
    "\n",
    "# ========== 参数设置 ==========\n",
    "symbol = \"恒生指数\"\n",
    "year_spans = [2, 3, 5, 8, 10, 13]\n",
    "\n",
    "# ========== 获取数据 ==========\n",
    "today = datetime.datetime.today()\n",
    "full_start_date = (today - pd.DateOffset(years=max(year_spans))).strftime('%Y%m%d')\n",
    "end_date = today.strftime('%Y%m%d')\n",
    "\n",
    "# 获取上证指数历史数据（英文字段）\n",
    "df = ak.stock_hk_index_daily_sina(symbol=\"CES100\")\n",
    "df[\"date\"] = pd.to_datetime(df[\"date\"])\n",
    "df = df.sort_values(\"date\")\n",
    "\n",
    "# ========== 初始化图表数据容器 ==========\n",
    "bar_data = {}  # key: N_YEARS, value: 月份 -> 上涨次数\n",
    "\n",
    "# ========== 各周期上涨统计 ==========\n",
    "for N_YEARS in year_spans:\n",
    "    start_cut = today - pd.DateOffset(years=N_YEARS)\n",
    "    df_cut = df[df[\"date\"] >= start_cut].copy()\n",
    "\n",
    "    # 每月开盘与收盘\n",
    "    monthly_df = df_cut.groupby(df_cut[\"date\"].dt.to_period(\"M\")).agg({\n",
    "        \"open\": \"first\",\n",
    "        \"close\": \"last\"\n",
    "    }).reset_index()\n",
    "\n",
    "    monthly_df[\"month\"] = monthly_df[\"date\"].dt.month\n",
    "    monthly_df[\"up\"] = monthly_df[\"close\"] > monthly_df[\"open\"]\n",
    "\n",
    "    up_counts = monthly_df.groupby(\"month\")[\"up\"].sum()\n",
    "    total_counts = monthly_df.groupby(\"month\")[\"up\"].count()\n",
    "\n",
    "    total_up_count = up_counts.sum()\n",
    "    total_months = len(monthly_df)\n",
    "    overall_up_probability = total_up_count / total_months if total_months else 0\n",
    "\n",
    "    print(f\"最近 {N_YEARS} 年上涨月数/总月数：{total_up_count}/{total_months} | 概率：{overall_up_probability:.2%}\")\n",
    "\n",
    "    # 保存当前周期上涨次数\n",
    "    bar_data[N_YEARS] = up_counts\n",
    "\n",
    "# ========== 合并图表：分组柱状图 ==========\n",
    "plt.figure(figsize=(12, 6))\n",
    "\n",
    "bar_width = 0.12\n",
    "months = np.arange(1, 13)\n",
    "offsets = np.linspace(-bar_width * len(year_spans) / 2, bar_width * len(year_spans) / 2, len(year_spans))\n",
    "\n",
    "for i, N_YEARS in enumerate(year_spans):\n",
    "    counts = bar_data.get(N_YEARS, pd.Series(0, index=range(1, 13)))\n",
    "    counts = counts.reindex(range(1, 13), fill_value=0)\n",
    "    plt.bar(months + offsets[i], counts.values, width=bar_width, label=f\"{N_YEARS}年\")\n",
    "\n",
    "plt.xticks(months, [f\"{m}月\" for m in months], rotation=-90)\n",
    "plt.xlabel(\"月份\")\n",
    "plt.ylabel(\"上涨次数\")\n",
    "plt.title(f\"{symbol} 不同周期每月上涨次数对比\")\n",
    "plt.legend(title=\"周期\")\n",
    "plt.grid(axis='y', linestyle='--', alpha=0.6)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  }
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